Quality of death certificates completion for COVID‐19 cases in the southeast of Iran: A cross‐sectional study

Abstract Background and Aim Death certificate (DC) data provides a basis for public health policies and statistics and contributes to the evaluation of a pandemic's evolution. This study aimed to evaluate the quality of the COVID‐19‐related DC completion. Methods A descriptive‐analytical study was conducted to review a total of 339 medical records and DCs issued for COVID‐19 cases from February 20 to September 21, 2020. A univariate analysis (χ 2 as an unadjusted analysis) was performed, and multiple logistic regression models (odd ratio [OR] and 95% confidence interval [CI] as adjusted analyses) were used to evaluate the associations between variables. Results Errors in DCs were classified as major and minor. All of the 339 examined DCs were erroneous; more than half of DCs (57.8%) had at least one major error; all of them had at least one minor error. Improper sequencing (49.3%), unacceptable underlying causes of death (UCOD) (33.3%), recording more than one cause per line (20.1%), listing general conditions instead of specific terms (11.2%), illegible handwriting (8.3%), competing causes (6.2%), and mechanisms (3.8%) were most common major errors, respectively. Absence of time interval (100%), listing mechanism allying with UCOD (51.6%), using abbreviations (45.4%), missing major comorbidities (16.5%), and listing major comorbidities in part I (16.5%) were most common minor errors, respectively. Conclusion The rate of both major and minor errors was high. Using automated tools for recording and selecting death cause(s), promoting certifiers' skills on DC completion, and applying quality control mechanisms in DC documentation can improve death data and statistics.


| INTRODUCTION
In compliance with the World Health Organization (WHO) guidelines, death certificates (DCs) in Iran consists of two parts. 1 DCs in Iran are completed only by physicians whether general practitioners (GPs) or specialists. 2 Part I includes four lines (a, b, c, and d), which are used for reporting diseases or conditions that form part of the sequence of events, leading directly to death (e.g., [ . Part II includes all conditions that are not included in part I, but contribute to death (e.g., diabetes mellitus). 1  Accurate mortality statistics are crucial for public health decisionmaking. However, the COVID-19 pandemic has highlighted the need for quality data, in particular concerning the quality of DC completion. 5 Also, data in DCs related to COVID-19 have a significant impact on local, regional, and national monitoring, planning, and policymaking and can help reduce the pandemic spread. 6 On the other hand, lack of reliable data on cause(s) of death can lead to inaccurate assessment and decision-making in public health and result in the delivery of low-quality health services. 7 DC completion errors have serious effects on death statistics. 1,8 Madadin et al. 9 showed that these errors are common in the Middle East. The quality of DCs completion related to COVID-19, as a source of pandemic death statistics, plays a key role in pandemic policymaking and management.
The quality of DCs related to COVID-19 determines the related public health policies and statistics, and provides an accurate understanding of the extent or progression of COVID-19. 6 The WHO encourages countries to use a standardized DC format by conforming to the International Form of Medical Certificate of Cause of Death (MCCD) to ensure the uniformity and quality of data and facilitate a global comparison. 10 The COVID-19 pandemic has posed many challenges to the collection of comparable and timely data on COVID-19 mortality rates in Europe; therefore, governments should prioritize timely collection, analysis, and report of mortality data. 11 However, in many cases, DCs do not provide an accurate description of the causes and contributing conditions, leading to a misunderstanding of the recorded conditions. Failure to register the contributing conditions, different definitions of death due to COVID-19, and various policies used to examine the disease affect the data comparability both nationally and internationally over time. 6 Disease prevention and control, besides efficient allocation of medical resources at national levels, depend on DC data 6 and surveillance system data. 12 Such information is the main determinant for quantifying the effects of COVID-19 pandemic. However, poorquality data can be a major obstacle in policymaking for public health authorities and planners in confronting future health emergencies. 13 The WHO has published international guidelines and instructions for completing and coding the causes of COVID-19 death. It has been emphasized that all COVID-19 related conditions should be recorded and coded qualitatively so that the statistics can be compared and analyzed at different national and international levels. 14 Therefore, quality assessment is the first and foremost step toward ensuring data quality. To the best of our knowledge, no study has been published on the completion accuracy of DCs related to COVID-19. Therefore, this study aimed to evaluate the completion quality of DCs related to COVID-19 in hospitals of Zahedan, Iran. Due to the lack of reports on COVID-19-related deaths in the perinatal period in our study population, this study was limited to COVID-19-related deaths which occurred after the perinatal period.
However, two DCs of the deceased sent to the post-mortem room were discarded due to lack of access. Finally, all certificates of inhospital deaths, except those requiring a post-mortem examination, were included in this study. These certificates were archived in the medical records department of the hospital from February 20 to

| Setting and population
A total of 339 COVID-19-related deaths occurred from February 20 to September 21, 2020, in Zahedan, Iran. All DCs obtained from the medical records department were selected and assessed for major and minor errors. We also collected the demographic characteristics of the decedents (e.g., sex, age, length of stay [LOS], ward, and death cause/month), certifiers' specialty, and cause(s) of death on DCs.

| Measures
We investigated eight major errors and five minor errors, similar to previous studies in the literature. 1

| Statistical analysis
Descriptive and analytical statistics were analyzed in SPSS version 11.0 (SPSS Inc.). In this study, the response variables included major and minor errors at two levels (0 = No and 1 = Yes); they were determined based on the sum of eight major errors and five minor errors. Age, sex, LOS, ward, month of death, comorbidity, and certifiers' specialty were the independent variables. To simplify the interpretation of test results, we categorized quantitative variables, such as age and LOS, into four categories. Besides, we divided the data into 7 months, three certifier specialties, four wards, and two comorbidity categories (Table 3 and Table 4). A univariate analysis (χ 2 as an unadjusted analysis) was performed, and multiple logistic regression models (odd ratio [OR] and 95% confidence interval [CI] as adjusted analyses) were used to evaluate the correlation between variables. A p < 0.05 was considered significant.

| Correlation between major errors with other variables
In the unadjusted analysis, gender (χ² Our unadjusted results revealed that the variables ward (χ² = 6.559, p = 0.087) and month of death (χ² = 11.631, p = 0.071) were statistically significant at <0.10 level. Almost the odds of a major error in all months were lower than in the initial month (Table 3). skill and knowledge about ill-defined conditions and those unlikely to cause death increases the likelihood of unacceptable UCODs.

| Correlation between minor errors with other variables
Overall, listing more than one cause per line in part I of DC was observed in 20.1% of the reviewed DCs, which is higher than some other studies 1,8,24 ; nevertheless, it was lower than those reported by Pokale and Karmarkar 26 and Hazard et al. 19 Overall, this error type can increase the possibility of recording the competing causes and incorrect coding of death causes.
The WHO necessitates certifiers to use specific conditions rather than general ones, because using the latter reduces the quality of mortality statistics. 10 In the present study, listing general conditions instead of specific ones was reported in 11.2% of DCs. Earlier studies have reported a range of 1%−56% for this error type. 1,24,29,32 Moreover, illegible handwriting was found in 8.3% of the reviewed DCs. In previous studies, the frequency of this error was estimated at 2.5%−40.3% in Iran, 1,3 10%−15% in India, 21,22 10.2% in Palestine, 25 and 2.5% in South Africa. 33 Although this error type only occurs in countries that use a manual system for registering DCs, it has a significant effect on misinterpreting the chain of events leading to death, selecting an incorrect UCOD, and ultimately reporting unreliable mortality statistics. The use of a carbon paper version of DCs in the patient record and coding based on it, beside the lack of a quality control mechanism for documenting DCs, can explain the high prevalence of this error type in Iran.
The frequency of errors in DCs related to competing causes (6.2%) was lower than previous studies conducted in Iran (range: 11.9%−27.5%) 1,3 and also most other countries (range: 9.5% −88%). 16 10 In more than half of DCs, the mechanism of death was followed by a proper UCOD (51.6%). Mechanism of death refers to physiological derangements such as cardiac arrest, respiratory arrest, and cardiopulmonary arrest caused by the cause of death. 29 This error type (range: 19%−80%) was common in earlier studies, 1,10,24,27,32 especially in India. 17,21,22,36 However, the death mechanism cannot explain the events preceding death, and it has no analytical value in public health and mortality statistics. Therefore, certifiers should not use terms indicating the death mechanism (e.g., organ failure and cardiac arrest) in completing DCs. 37 The present study showed that in 45.4% of DCs, abbreviations were used to describe conditions, which is in line with some previous studies, 1,16,22,24 but higher than 8,28 and lower than 17,19,31 some others; the lack of training on the instructions and the certifiers' inattention to completing DCs can justify the prevalence of this error type. The registration of comorbidities in DCs is crucial because of their analytical value to develop strategies to prevent, control, and thus reduce mortality. 38 In this context, comorbidities refer to all diseases or conditions contributing to death that were not reported in the chain of events in part I and did not result in the UCOD.
Comorbidities should be reported in part II of DCs (e.g., diabetes mellitus type 1, chronic obstructive pulmonary disease, and hypertension). 10 The frequency of errors related to missing major comorbidities associated with death and listing major comorbidities/contributing cause(s) in part I of DCs was 16.5%, which is much lower than previous studies. 1,8,16,24,32 This can be explained by the impact of comorbidities on the progression of COVID-19 and the emphasis of the WHO and Iran's MOHME on recording comorbidities to control the pandemic and reduce its casualties. Also, a significant association was found between the decedents' comorbidities and both major and minor errors; therefore, DCs of decedents with comorbidities were more prone to both major and minor errors.
Given the high rate of errors in the examined COVID-19 DCs, the measured statistics should be used cautiously.
Previous studies [15][16][17]20,29,39,40 have reported that certifiers' education has a substantial impact on the quality of DC completion. Therefore, improving the certifiers' knowledge and skills for completing DCs according to the WHO guidelines, using a robust quality control mechanism for DC documentation, and planning automated systems for recording, selecting, and coding the death causes can play a key role in enhancing the completion quality of DCs, their coding, and finally, the extracted mortality statistics.

| LIMITATIONS
Considering the paper-based format of DCs, besides the manual selection of cause(s) of death and their coding in Iran, the results of this study can be only generalized to countries with a similar death registration mechanism.

| CONCLUSION
More than half of the DCs had at least one major error, while all of them had at least one minor error. Improper sequencing of conditions, unacceptable UCODs, recording more than one cause per line, listing general conditions rather than specific ones, illegible handwriting, competing causes, and listing the mechanism of death without a proper UCOD were the most common major errors, respectively. Also, the absence of time intervals between the disease onset and death, mechanism of death followed by a proper UCOD, using abbreviations, and missing major comorbidities/listing major comorbidities in part I of DCs were the most common minor errors,